Attendees will be introduced to the fundamental capabilities of in-memory computing platforms (IMCPs). IMCPs boost application performance and solve scalability problems by storing and processing unlimited data sets distributed across a cluster of interconnected machines.

Apache Ignite® and GridGain® allow you to perform fast calculations and run highly efficient queries over distributed data. Both Ignite and GridGain provide a flexible configuration that can help you make cluster operations more secure. In this webinar, we will cover the following security topics:

Change Data Capture (CDC) has become a very efficient way to automate and simplify the ETL process for data synchronization between disjointed databases. It is also a useful tool for efficient replication schemas. We will cover the fundamental principles and restrictions of CDC and review examples of how change data capture is implemented in real life use cases.

With most machine learning (ML) and deep learning (DL) frameworks, it can take hours to move data and to train models. It can also be hard to scale with data sets that are increasingly frequently larger than the capacity of any single server. The size of the data can also make it hard to incrementally test and retrain models in near real-time to improve business results.

Deployment models for Apache Ignite® and applications connected to it vary depending on the target production environment. A bare metal environment provides the most flexibility and fewer restrictions on configuration options.

To take full advantage of an in-memory platform, it’s often not enough to upload your data into a cluster and start querying it with key-value or SQL APIs. You need to distribute the data efficiently and tap into distributed computations that minimize data movement over the network.

Apache Ignite is a powerful in-memory computing platform. The Apache IgniteSink streaming connector enables users to inject Flink data into the Ignite cache. Join Saikat Maitra to learn how to build a simple data streaming application using Apache Flink and Apache Ignite.

If you experience limitations with the size, scale or performance of your relational database, it may be time to migrate to a distributed system. Apache Ignite is a distributed platform that can function as a database, providing both SQL and JCache APIs to work with your data. In this webinar, we will consider real-world examples and discuss pros and cons of each approach, especially:

In this webinar you will learn how to use the service grid capabilities of the Apache Ignite distributed in-memory computing platform. Simple code examples will help us review possible architectural solutions and demonstrate how to build fault-tolerant, scalable and flexible systems.

Most enterprises have PostgreSQL deployments that they will be using for years to come for transactional, big data, mobile, and IoT use cases. How can Postgres continue to support the current and emerging use cases which demand ever higher performance and more scalability into the future?

GridGain Web Console is an interactive configuration, management, and monitoring tool for Apache® Ignite™ and GridGain®. During this session we will cover the basics of installation and configuration as we walk through the Web Console feature list.

Learn some of the best practices and the different options for maximizing availability and preventing data loss. This session explains in detail the various challenges including cluster and data center failures, and the best practices for implementing disaster recovery (DR) for distributed in-memory computing based on real-world deployments. Topics include:

This webinar discusses deploying Apache Ignite into production in public and private clouds. Companies have faced many challenges when deploying in-memory computing platforms such as Apache Ignite in the cloud, but they have also discovered many best practices that have made success possible.

This in-memory computing best practices webinar explains how companies add in-process Hybrid Transactional/Analytical Processing (HTAP) architectures for real-time data access, analytics, and decision automation to their existing applications and analytics systems.

This webinar discusses how an in-memory computing platform such as GridGain or Apache Ignite can modernize existing data lake architectures, enabling real-time analytics that spans operational, historical, and streaming data sets.

Learn how to monitor various components of a distributed cluster for network, memory, or node-specific issues, and troubleshoot to resolve issues. By the end of this session you'll have a handy checklist and set of tools to consider using for your own deployments.

Learn how companies have added speed and scale to MySQL deployments for different use cases. This webinar will cover the various options available and when each option makes sense. It will also cover how to evolve your architecture over time to add the speed, scale, agility and new technologies needed for digital transformation and other initiatives.

As an in-memory computing platform, GridGain® and Apache Ignite support native persistence that stores data and indexes transparently on non-volatile memory, SSD or disk. This webinar provides insights into the underlying architecture and best practices for implementing native persistence in production.

Learn how companies have added speed and scale to PostgreSQL deployments for different use cases. This webinar will cover the various options available and when each option makes sense. It will also cover how to evolve your architecture over time to add the speed, scale, agility and new technologies needed for digital transformation and other initiatives.

Guaranteeing that your in-memory computing solution stays up and running is the most important goal for a rolling out a new production environment. In this webinar, we will review a going-into-production checklist to make sure you have all the bases covered and have thought through all your requirements, needs and potential roadblocks.

With most machine learning (ML) and deep learning (DL) frameworks, it can take hours to move data, and hours to train models. Learn how Apache Ignite eliminates runs model training and execution in near-real-time and makes continuous learning possible.

In this webinar, you will learn how to add speed and scale to your Oracle Database, Oracle-based applications, APIs and analytics for different use cases. We will discuss when each option makes sense, as well as how to evolve your architecture over time to add the speed, scale, agility and new technologies needed for digital transformation and other initiatives.

Learn what's new with Apache Ignite 2.7. This session, given by Denis Magda, Apache Ignite PMC Chair, is for all Apache Ignite users. You will learn how the new capabilities of Apache Ignite work. You will also understand more about some of the other changes made to Apache Ignite, and the reasoning behind them. Come with your questions, and learn from the questions of your peers.

Regardless of how mature a data storage technology is, backing up data is a laborious and difficult task that can cost us time, increase our stress levels and jeopardize our jobs. During this webinar, you will learn how to perform data snapshot without impacting ongoing user activities, how to perform a snapshot whilst keeping data consistent and transactionally complete across the cluster... 

Learn how combining Apache Ignite with a Relational DBMS can offer enterprises the best of both open-source worlds. This webinar will discuss how this combination produces a highly-scalable high-velocity grid-based in-memory SQL database, with a robust fully-featured SQL persistent datastore for advanced analytics and data-warehouse capabilities.

Digital transformations are arguably the most important initiatives for companies. They can literally make or break a business.  But transformation is not easy because there’s a big digital divide between the speed, scale and computing needed for new digital channels and APIs, and what existing systems can deliver. Learn how leading digital innovators have solved these problems by using in-memory computing, and the roadmaps that worked for them.

Learn some of the best practices companies have used for making Apache Ignite and Apache Kafka scale. Making stream processing scale requires making all the components—including messaging, processing, storage—scale together.   During this 1-hour webinar by GridGain Systems Professional Services Consultant Alexey Kukushkin, you will learn about:

The need for real-time computing has resulted in the growth of many different in-memory computing (IMC) technologies. This includes caches, in-memory data grids, in-memory databases, streaming technologies and broader IMC platforms.  But what are the best technologies for each type of project? Learn about your options from one of the leading IMC veterans.

It used to be that the only way to improve application performance was to add a cache. But caches like Redis don't understand SQL. They require you to modify your applications with non-SQL coding and data models, and copy and synch data across two different models. They don't support ACID transactions very well. And they have their limits when it comes to scalability.

This eBook, Part 1 in the In-Memory Computing for Financial Services eBook Series, discusses how financial service firms are using in-memory computing platforms such as GridGain and Apache® Ignite™ to address the challenges of high-frequency trading, fraud prevention and real-time regulatory compliance.